import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
   import torch_xla.debug.metrics as met




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   Dict							, *lowercase_				:   List[Any]							, lowercase_				:   List[str]=None							, lowercase_				:   int=None							, **lowercase_				:   Union[str, Any])						->	Tuple:


   """simple docstring"""







   super().__init__(*lowercase_							, **lowercase_)
   _UpperCamelCase           =    eval_examples
   _UpperCamelCase           =    post_process_function







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   List[str]=None							, lowercase_				:   Tuple=None							, lowercase_				:   Any=None							, lowercase_				:   str = "eval")						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    self.eval_dataset if eval_dataset is None else eval_dataset
   _UpperCamelCase           =    self.get_eval_dataloader(lowercase_)
   _UpperCamelCase           =    self.eval_examples if eval_examples is None else eval_examples

   # Temporarily disable metric computation, we will do it in the loop here.
   _UpperCamelCase           =    self.compute_metrics
   _UpperCamelCase           =    None
   _UpperCamelCase           =    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
   _UpperCamelCase           =    time.time()
   try:
     _UpperCamelCase           =    eval_loop(
         lowercase_							, description="Evaluation"							, prediction_loss_only=True if compute_metrics is None else None							, ignore_keys=lowercase_							, metric_key_prefix=lowercase_							, )
   finally:
     _UpperCamelCase           =    compute_metrics
   _UpperCamelCase           =    self.args.eval_batch_size * self.args.world_size
   if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
     start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
   output.metrics.update(
       speed_metrics(
           lowercase_							, lowercase_							, num_samples=output.num_samples							, num_steps=math.ceil(output.num_samples / total_batch_size)							, ))
   if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
     # Only the main node write the results by default
     _UpperCamelCase           =    self.post_process_function(lowercase_							, lowercase_							, output.predictions)
     _UpperCamelCase           =    self.compute_metrics(lowercase_)

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()):
       if not key.startswith(f'{metric_key_prefix}_'):
         _UpperCamelCase           =    metrics.pop(lowercase_)
     metrics.update(output.metrics)
   else:
     _UpperCamelCase           =    output.metrics

   if self.args.should_log:
     # Only the main node log the results by default
     self.log(lowercase_)

   if self.args.tpu_metrics_debug or self.args.debug:
     # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
     xm.master_print(met.metrics_report())

   _UpperCamelCase           =    self.callback_handler.on_evaluate(self.args							, self.state							, self.control							, lowercase_)
   return metrics







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   List[str]							, lowercase_				:   List[Any]							, lowercase_				:   str=None							, lowercase_				:   str = "test")						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.get_test_dataloader(lowercase_)

   # Temporarily disable metric computation, we will do it in the loop here.
   _UpperCamelCase           =    self.compute_metrics
   _UpperCamelCase           =    None
   _UpperCamelCase           =    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
   _UpperCamelCase           =    time.time()
   try:
     _UpperCamelCase           =    eval_loop(
         lowercase_							, description="Prediction"							, prediction_loss_only=True if compute_metrics is None else None							, ignore_keys=lowercase_							, metric_key_prefix=lowercase_							, )
   finally:
     _UpperCamelCase           =    compute_metrics
   _UpperCamelCase           =    self.args.eval_batch_size * self.args.world_size
   if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
     start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
   output.metrics.update(
       speed_metrics(
           lowercase_							, lowercase_							, num_samples=output.num_samples							, num_steps=math.ceil(output.num_samples / total_batch_size)							, ))

   if self.post_process_function is None or self.compute_metrics is None:
     return output

   _UpperCamelCase           =    self.post_process_function(lowercase_							, lowercase_							, output.predictions							, "predict")
   _UpperCamelCase           =    self.compute_metrics(lowercase_)

   # Prefix all keys with metric_key_prefix + '_'
   for key in list(metrics.keys()):
     if not key.startswith(f'{metric_key_prefix}_'):
       _UpperCamelCase           =    metrics.pop(lowercase_)
   metrics.update(output.metrics)
   return PredictionOutput(predictions=predictions.predictions							, label_ids=predictions.label_ids							, metrics=lowercase_)





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ = None    ,     a__ = None    ,     a__ = False    ,     )      ->tuple[int, float, str]:




  '''simple docstring'''

  _UpperCamelCase           =    cipher_alphabet or [chr(a__ ) for i in range(97    ,     123 )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    _UpperCamelCase           =    {
        "a": 0.08497,
        "b": 0.01492,
        "c": 0.02202,
        "d": 0.04253,
        "e": 0.11162,
        "f": 0.02228,
        "g": 0.02015,
        "h": 0.06094,
        "i": 0.07546,
        "j": 0.00153,
        "k": 0.01292,
        "l": 0.04025,
        "m": 0.02406,
        "n": 0.06749,
        "o": 0.07507,
        "p": 0.01929,
        "q": 0.00095,
        "r": 0.07587,
        "s": 0.06327,
        "t": 0.09356,
        "u": 0.02758,
        "v": 0.00978,
        "w": 0.02560,
        "x": 0.00150,
        "y": 0.01994,
        "z": 0.00077,
    }
  else:
    # Custom frequencies dictionary
    _UpperCamelCase           =    frequencies_dict

  if not case_sensitive:
    _UpperCamelCase           =    ciphertext.lower()

  # Chi squared statistic values
  _UpperCamelCase           =    {}

  # cycle through all of the shifts
  for shift in range(len(a__ ) ):
    _UpperCamelCase           =    ""

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        _UpperCamelCase           =    (alphabet_letters.index(letter.lower() ) - shift) % len(
            a__ )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    _UpperCamelCase           =    0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        _UpperCamelCase           =    letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.lower().count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    _UpperCamelCase           =    (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(a__ ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  _UpperCamelCase           =    min(
      a__    ,     key=a__    ,     )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      _UpperCamelCase
  )      ,						(
      _UpperCamelCase
  )      ,						
  )           =    chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )




